SoroushVahidi/scidocs huggingface dataset:
Stacked Semantics-Guided Attention Model for Fine-Grained Zero-Shot Learning
(2018)

Zero-Shot Learning (ZSL) is generally achieved via aligning the semantic relationships between the visual features and the corresponding class semantic descriptions. However, using the global features to represent fine-grained images may lead to sub-optimal results since they neglect the discriminative differences of local regions. Besides, different regions contain distinct discriminative information. The important regions should contribute more to the prediction. To this end, we propose a novel stacked semantics-guided attention (SGA) model to obtain semantic relevant features by using individual class semantic features to progressively guide the visual features to generate an attention map for weighting the importance of different local regions. Feeding both the integrated visual features and the class semantic features into a multi-class classification architecture, the proposed framework can be trained end-to-end. Extensive experimental results on CUB and NABird datasets show that the proposed approach has a consistent improvement on both fine-grained zero-shot classification and retrieval tasks.
Monitoring of gait performance using dynamic time warping on IMU-sensor data
(2016)

In this paper, a novel method for monitoring the changes in gait joint angle trajectories recorded using the low-cost and wearable Inertial Measurement Units (IMU) is presented. The introduced method is based on Dynamic Time Warping (DTW), an algorithm commonly used for evaluating the similarity of two time series which may vary in time and speed. DTW is employed as the measure of distance between two gait trajectories taken in different time instances, which could be used as an intuitive and effective measure for the evaluation of gait performances. The experimental results presented in the paper demonstrate that the proposed method is applicable for clinically relevant applications and is consequently adaptable to patients with diseases characterized with gait disorders and to different walking scenarios. The proposed method was firstly validated by applying the DTW-based measure on gait trajectories of five healthy subjects recorded while simulating different levels of walking disabilities. Then proposed measure was applied to estimate the distance between the “healthy” gait trajectories and gait trajectories of three patients with Parkinson's disease (PD) while performing single-task and dual-task overground walking. Also, the proposed measure was demonstrated as an effective measure for monitoring the changes in gait patterns of a PD patient before and after medication-based treatment. This result indicates potential use of proposed method for effective pharmacological management of PD.
Evolved Machines Shed Light on Robustness and Resilience
(2014)

In biomimetic engineering, we may take inspiration from the products of biological evolution: we may instantiate biologically realistic neural architectures and algorithms in robots, or we may construct robots with morphologies that are found in nature. Alternatively, we may take inspiration from the process of evolution: we may evolve populations of robots in simulation and then manufacture physical versions of the most interesting or more capable robots that evolve. If we follow this latter approach and evolve both the neural and morphological subsystems of machines, we can perform controlled experiments that provide unique insight into how bodies and brains can work together to produce adaptive behavior, regardless of whether such bodies and brains are instantiated in a biological or technological substrate. In this paper, we review selected projects that use such methods to investigate the synergies and tradeoffs between neural architecture, morphology, action, and adaptive behavior.
An Introduction to Locally Linear Embedding
(2000)

Many problemsin informationprocessinginvolvesomeform of dimensionality reduction.Herewe describelocally linearembedding(LLE), anunsupervisedlearningalgorithmthat computeslow dimensional,neighborhood preservingembeddingsof high dimensionaldata.LLE attemptsto discover nonlinearstructurein high dimensionaldataby exploiting thelocal symmetries of linear reconstructions.Notably, LLE mapsits inputs into a single global coordinatesystemof lower dimensionality, and its optimizations— thoughcapableof generatinghighly nonlinearembeddings—donot involve localminima.We illustratethemethodon imagesof lips usedin audiovisual speechsynthesis.
RECENT DEVELOPMENTS IN PAPER CURRENCY RECOGNITION SYSTEM
(2013)

Currency denomination recognition is one the active research topics at present. And this wide interest is particularly due to the various potential applications it has. Monetary transaction is an integral part of our day to day activities. However, blind people particularly suffer in monetary transactions. They are not able to effectively distinguish between various denominations and are often deceived by other people. Also, a reliable currency recognition system could be used in any sector wherever monetary transaction is of concern. Thus, there is an ardent need to design a system that is helpful in recognition of paper currency notes correctly. Currency denomination detection is a vast area of research and significant progress had been achieved over the years. This paper presents an extensive survey of research on various developments in recent years in identification of currency denomination. A number of techniques applied by various researchers are discussed briefly in order to assess the state of art.
The effect of substrate noise on VCO performance
(2005)

This study characterizes the effect of substrate noise on a standard component of the RF front end: the voltage controlled oscillator (VCO), as well as evaluating the effect of VCO bias current and guard rings on noise performance. Frequency effects of substrate noise are also examined through the study of VCOs at three different center frequencies: 900 MHz, 2.4 GHz, and 5.2 GHz. Substrate noise is a serious problem that continues to plague mixed-signal designs. Components of the RF frontend are particularly sensitive to substrate noise as the effectiveness of standard isolation techniques degrades at higher frequencies. This study has shown that the phase noise of a VCO is adversely affected by substrate noise. In the extreme, the VCO can lock to the substrate noise. Guard rings can effectively attenuate substrate noise at lower frequencies. For example, at 900 MHz, as much as 25 dB of isolation is observed. At 5.2 GHz, the isolation reduces to 10 dB. Furthermore, the use of guard rings can improve the response of the VCO to injection locking.
Delay-Constrained Hybrid Computation Offloading With Cloud and Fog Computing
(2017)

To satisfy the delay constraint, the computation tasks can be offloaded to some computing servers, referred to as offloading destinations. Different to most of existing works which usually consider only a single type of offloading destinations, in this paper, we study the hybrid computation offloading problem considering diverse computation and communication capabilities of two types of offloading destinations, i.e., cloud computing servers and fog computing servers. The aim is to minimize the total energy consumption for both communication and computation while completing the computation tasks within a given delay constraint. It is quite challenging because the delay cannot be easily formulated as an explicit expression but depends on the embedded communication-computation scheduling problem for the computation offloading to different destinations. To solve the computation offloading problem, we first define a new concept named computation energy efficiency and divide the problem into four subproblems according to the computation energy efficiency of different types of computation offloading and the maximum tolerable delay. For each subproblem, we give a closed-form computation offloading solution with the analysis of communication-computation scheduling under the delay constraint. The numerical results show that the proposed hybrid computation offloading solution achieves lower energy consumption than the conventional single-type computation offloading under the delay constraint.
Information Security: End User Behavior and Corporate Culture
(2007)

Information is the life blood of all modern organizations yet the news media continue to report stories of critical information loss. The purpose of information security is to protect valuable assets, such as information, hardware, software and people. The majority of information security specialists believe that promoting good end user behavior and constraining bad end user behavior is an important component of an effective Information Security Management System (ISMS). Implementing effective information security involves understanding security-related risk, then developing and implementing appropriate controls. In general the better employees are at applying the controls the more secure the organization will be, because even the best designed technical controls and procedures will be of limited value if the staff involved do not understand why they have been implemented and what they are accomplishing. Achieving the required level of understanding usually requires more than an annual awareness training initiative and represents a major challenge for most organizations. In fact, for many organizations it will involve a cultural change to ensure the integration of information security concepts into the organizational culture.
Robust visual inertial odometry using a direct EKF-based approach
(2015)

In this paper, we present a monocular visual-inertial odometry algorithm which, by directly using pixel intensity errors of image patches, achieves accurate tracking performance while exhibiting a very high level of robustness. After detection, the tracking of the multilevel patch features is closely coupled to the underlying extended Kalman filter (EKF) by directly using the intensity errors as innovation term during the update step. We follow a purely robocentric approach where the location of 3D landmarks are always estimated with respect to the current camera pose. Furthermore, we decompose landmark positions into a bearing vector and a distance parametrization whereby we employ a minimal representation of differences on a corresponding σ-Algebra in order to achieve better consistency and to improve the computational performance. Due to the robocentric, inverse-distance landmark parametrization, the framework does not require any initialization procedure, leading to a truly power-up-and-go state estimation system. The presented approach is successfully evaluated in a set of highly dynamic hand-held experiments as well as directly employed in the control loop of a multirotor unmanned aerial vehicle (UAV).
IoT-based continuous glucose monitoring system: A feasibility study
(2017)

Health monitoring systems based on Internet-of-things (IoT) have been recently introduced to improve the quality of health care services. However, the number of advanced IoT-based continuous glucose monitoring systems is small and the existing systems have several limitations. In this paper we study feasibility of invasive and continuous glucose monitoring (CGM) system utilizing IoT based approach. We designed an IoT-based system architecture from a sensor device to a back-end system for presenting real-time glucose, body temperature and contextual data (i.e. environmental temperature) in graphical and human-readable forms to end-users such as patients and doctors. In addition, nRF communication protocol is customized for suiting to the glucose monitoring system and achieving a high level of energy efficiency. Furthermore, we investigate energy consumption of the sensor device and design energy harvesting units for the device. Finally, the work provides many advanced services at a gateway level such as a push notification service for notifying patient and doctors in case of abnormal situations (i.e. too low or too high glucose level). The results show that our system is able to achieve continuous glucose monitoring remotely in real-time. In addition, the results reveal that a high level of energy efficiency can be achieved by applying the customized nRF component, the power management unit and the energy harvesting unit altogether in the sensor device. c © 2017 The Authors. Published by E sevier B.V. i ilit f t f re ce r ra hairs.
Predicting Categorical Emotions by Jointly Learning Primary and Secondary Emotions through Multitask Learning
(2018)

Detection of human emotions is an essential part of affect-aware human-computer interaction (HCI). In daily conversations, the preferred way of describing affects is by using categorical emotion labels (e.g., sad, anger, surprise). In categorical emotion classification, multiple descriptors (with different degrees of relevance) can be assigned to a sample. Perceptual evaluations have relied on primary and secondary emotions to capture the ambiguous nature of spontaneous recordings. Primary emotion is the most relevant category felt by the evaluator. Secondary emotions capture other emotional cues also conveyed in the stimulus. In most cases, the labels collected from the secondary emotions are discarded, since assigning a single class label to a sample is preferred from an application perspective. In this work, we take advantage of both types of annotations to improve the performance of emotion classification. We collect the labels from all the annotations available for a sample and generate primary and secondary emotion labels. A classifier is then trained using multitask learning with both primary and secondary emotions. We experimentally show that considering secondary emotion labels during the learning process leads to relative improvements of 7.9% in F1-score for an 8-class emotion classification task.
A New Concept Varistor With Epoxy/Microvaristor Composite
(2014)

A new concept composite varistor is proposed in this paper. This composite varistor has the chains of microvaristors, which are formed by applying an electric field during the curing process and work as current paths. This composite varistor shows superior nonlinear voltage-current characteristics despite small microvaristor content and has a good response against a steep voltage surge of several tens of nanoseconds in front time. In comparison with ceramic varistors where shapes are usually formed at their sintering temperatures of around 1000°C, the composite varistor has an advantage in flexible shape because its shape is formed by curing the base polymer at a low temperature.
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
(2010)

Model selection strategies for machine learning algorithm s typically involve the numerical optimisation of an appropriate model selection criterion, ofte n based on an estimator of generalisation performance, such as k-fold cross-validation. The error of such an estimator can b e broken down into bias and variance components. While unbiasedness is oft en cited as a beneficial quality of a model selection criterion, we demonstrate that a low varian ce is at least as important, as a nonnegligible variance introduces the potential for over-fitt ing in model selection as well as in training the model. While this observation is in hindsight perhaps rat her obvious, the degradation in performance due to over-fitting the model selection criterion can b e surprisingly large, an observation that appears to have received little attention in the machine lea rning literature to date. In this paper, we show that the effects of this form of over-fitting are often of c mparable magnitude to differences in performance between learning algorithms, and thus canno t be ignored in empirical evaluation. Furthermore, we show that some common performance evaluati on practices are susceptible to a form of selection bias as a result of this form of over-fitting and hence are unreliable. We discuss methods to avoid over-fitting in model selection and sub sequent selection bias in performance evaluation, which we hope will be incorporated into best pra ctice. While this study concentrates on cross-validation based model selection, the findings are quit general and apply to any model selection practice involving the optimisation of a model se lection criterion evaluated over a finite sample of data, including maximisation of the Bayesian evid nce and optimisation of performance bounds.
Design of a 1.8V-input 6.5V-output digital level shifter for trimming application
(2017)

A 1.8V input, 6.5V output step-up digital level shifter is proposed. It is based on contention mitigated digital level shifter (CMLS) but it uses single supply only instead of two. It uses a complete, stand-alone, and unregulated voltage charge pump circuit that generates a 6.5V output voltage from a 1.8V input. Charge pump output is then used as high-side supply (VDDH) of the step-up level shifter, while the low-side supply (VDDl) is externally-supplied 1.8V. Compared to the conventional level shifter where two separate supply voltages (VDDl and VDDH) are required, the proposed design uses a single supply to shifted-up a digital signals. This technique simplifies the user-end power supply requirement of the system, thus the proposed level shifter design is applicable in many low-power and power-constraint applications such as in mobile and portable devices application. The proposed design is implemented using 0.35-μm TSMC CMOS technology. The schematic and the nestlist have been designed and extracted using LTSPICE tool; while the simulation has been carried out using the HSPICE circuit simulator.
Lazy Theta*: Any-Angle Path Planning and Path Length Analysis in 3D
(2010)

Grids with blocked and unblocked cells are often used to represent continuous 2D and 3D environments in robotics and video games. The shortest paths formed by the edges of 8neighbor 2D grids can be up to ≈ 8% longer than the shortest paths in the continuous environment. Theta* typically finds much shorter paths than that by propagating information along graph edges (to achieve short runtimes) without constraining paths to be formed by graph edges (to find short “any-angle” paths). We show in this paper that the shortest paths formed by the edges of 26-neighbor 3D grids can be ≈ 13% longer than the shortest paths in the continuous environment, which highlights the need for smart path planning algorithms in 3D. Theta* can be applied to 3D grids in a straight-forward manner, but it performs a line-of-sight check for each unexpanded visible neighbor of each expanded vertex and thus it performs many more line-of-sight checks per expanded vertex on a 26-neighbor 3D grid than on an 8-neighbor 2D grid. We therefore introduce Lazy Theta*, a variant of Theta* which uses lazy evaluation to perform only one line-of-sight check per expanded vertex (but with slightly more expanded vertices). We show experimentally that Lazy Theta* finds paths faster than Theta* on 26-neighbor 3D grids, with one order of magnitude fewer line-of-sight checks and without an increase in path length.
Mechanisms of facial emotion recognition in autism spectrum disorders: Insights from eye tracking and electroencephalography
(2017)

While behavioural difficulties in facial emotion recognition (FER) have been observed in individuals with Autism Spectrum Disorder (ASD), behavioural studies alone are not suited to elucidate the specific nature of FER challenges in ASD. Eye tracking (ET) and electroencephalography (EEG) provide insights in to the attentional and neurological correlates of performance, and may therefore provide insight in to the mechanisms underpinning FER in ASD. Given that these processes develop over the course of the developmental trajectory, there is a need to synthesise findings in regard to the developmental stages to determine how the maturation of these systems may impact FER in ASD. We conducted a systematic review of fifty-four studies investigating ET or EEG meeting inclusion criteria. Findings indicate divergence of visual processing pathways in individuals with ASD. Altered function of the social brain in ASD impacts the processing of facial emotion across the developmental trajectory, resulting in observable differences in ET and EEG outcomes.
Adaptive Deep Brain Stimulation In Advanced Parkinson Disease
(2013)

OBJECTIVE
Brain-computer interfaces (BCIs) could potentially be used to interact with pathological brain signals to intervene and ameliorate their effects in disease states. Here, we provide proof-of-principle of this approach by using a BCI to interpret pathological brain activity in patients with advanced Parkinson disease (PD) and to use this feedback to control when therapeutic deep brain stimulation (DBS) is delivered. Our goal was to demonstrate that by personalizing and optimizing stimulation in real time, we could improve on both the efficacy and efficiency of conventional continuous DBS.


METHODS
We tested BCI-controlled adaptive DBS (aDBS) of the subthalamic nucleus in 8 PD patients. Feedback was provided by processing of the local field potentials recorded directly from the stimulation electrodes. The results were compared to no stimulation, conventional continuous stimulation (cDBS), and random intermittent stimulation. Both unblinded and blinded clinical assessments of motor effect were performed using the Unified Parkinson's Disease Rating Scale.


RESULTS
Motor scores improved by 66% (unblinded) and 50% (blinded) during aDBS, which were 29% (p = 0.03) and 27% (p = 0.005) better than cDBS, respectively. These improvements were achieved with a 56% reduction in stimulation time compared to cDBS, and a corresponding reduction in energy requirements (p < 0.001). aDBS was also more effective than no stimulation and random intermittent stimulation.


INTERPRETATION
BCI-controlled DBS is tractable and can be more efficient and efficacious than conventional continuous neuromodulation for PD.
Image Texture Feature Extraction Using GLCM Approach
(2013)

Feature Extraction is a method of capturing visual content of images for indexing & retrieval. Primitive or low level image features can be either general features, such as extraction of color, texture and shape or domain specific features. This paper presents an application of gray level co-occurrence matrix (GLCM) to extract second order statistical texture features for motion estimation of images. The Four features namely, Angular Second Moment, Correlation, Inverse Difference Moment, and Entropy are computed using Xilinx FPGA. The results show that these texture features have high discrimination accuracy, requires less computation time and hence efficiently used for real time Pattern recognition applications.
Plant Leaf Recognition using Shape Based Features and Neural Network Classifiers
(2011)

This paper proposes an automated system for recognizing plant species based on leaf images. Plant leaf images corresponding to three plant types, are analyzed using two different shape modeling techniques, the first based on the Moments-Invariant (M-I) model and the second on the CentroidRadii (C-R) model. For the M-I model the first four normalized central moments have been considered and studied in various combinations viz. individually, in joint 2-D and 3-D feature spaces for producing optimum results. For the C-R model an edge detector has been used to identify the boundary of the leaf shape and 36 radii at 10 degree angular separation have been used to build the feature vector. To further improve the accuracy, a hybrid set of features involving both the M-I and C-R models has been generated and explored to find whether the combination feature vector can lead to better performance. Neural networks are used as classifiers for discrimination. The data set consists of 180 images divided into three classes with 60 images each. Accuracies ranging from 90%-100% are obtained which are comparable to the best figures reported in extant literature. Keywords-plant recognition; moment invariants; centroid-radii model; neural network; computer vision.